LagunAR: A City-Scale Mobile Outdoor Augmented Reality Application for Heritage Dissemination.

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest.

A framework for designing interactive mobile training course content using augmented reality.

Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.

ARAM: A Technology Acceptance Model to Ascertain the Behavioural Intention to Use Augmented Reality.

The expansion of augmented reality across society, its availability in mobile platforms and the novelty character it embodies by appearing in a growing number of areas, have raised new questions related to people's predisposition to use this technology in their daily life. Acceptance models, which have been updated following technological breakthroughs and society changes, are known to be great tools for predicting the intention to use a new technological system. This paper proposes a new acceptance model aiming to ascertain the intention to use augmented reality technology in heritage sites-the Augmented Reality Acceptance Model (ARAM). ARAM relies on the use of the Unified Theory of Acceptance and Use of Technology model (UTAUT) model's constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, to which the new and adapted constructs of trust expectancy, technological innovation, computer anxiety and hedonic motivation are added. This model was validated with data gathered from 528 participants. Results confirm ARAM as a reliable tool to determine the acceptance of augmented reality technology for usage in cultural heritage sites. The direct impact of performance expectancy, facilitating conditions and hedonic motivation is validated as having a positive influence on behavioural intention. Trust expectancy and technological innovation are demonstrated to have a positive influence on performance expectancy whereas hedonic motivation is negatively influenced by effort expectancy and by computer anxiety. The research, thus, supports ARAM as a suitable model to ascertain the behavioural intention to use augmented reality in new areas of activity.

Benchmarking Built-In Tracking Systems for Indoor AR Applications on Popular Mobile Devices.

As one of the most promising technologies for next-generation mobile platforms, Augmented Reality (AR) has the potential to radically change the way users interact with real environments enriched with various digital information. To achieve this potential, it is of fundamental importance to track and maintain accurate registration between real and computer-generated objects. Thus, it is crucially important to assess tracking capabilities. In this paper, we present a benchmark evaluation of the tracking performances of some of the most popular AR handheld devices, which can be regarded as a representative set of devices for sale in the global market. In particular, eight different next-gen devices including smartphones and tablets were considered. Experiments were conducted in a laboratory by adopting an external tracking system. The experimental methodology consisted of three main stages: calibration, data acquisition, and data evaluation. The results of the experimentation showed that the selected devices, in combination with the AR SDKs, have different tracking performances depending on the covered trajectory.

Mobile augmented reality adapted to the ARCS model of motivation: a case study during the COVID-19 pandemic.

The rushed introduction of online education for universities because of the current covid-19 health crisis, has started to affect the quality of education for millions of students around the world. This pandemic has emphasized the need to improve the teaching process through the use of innovating educational tools, such as mobile augmented reality (mAR). This pilot study intends to evaluate the relationship between motivation and meaningful learning for university students through mAR, as well as the effects and implications of its use how supporting teaching activities in an Industrial Design and Technical Drawing course. A quantitative method research approach was applied to collect, process, and analyze the research data of the students' perception under the health restrictions caused by the current pandemic, using the Instructional Materials Motivation Survey (IMMS). The total number of questionnaires collected was 96, applied to students of the Industrial Engineering major at Universidad Católica del Maule in Chile. The instrument's reliability was measured using Cronbach's alpha, giving an overall value of 0.89. The implementation of an instructional model called ARCS (Attention-Relevance-Confidence-Satisfaction) was achieved. The implementation of mRA was positively valued by most of the students surveyed. An increase the percentage of students who achieved the expected learning objectives was found, in compared to previous versions of the course (without mAR). Finally, the results demonstrate a positive relation between mAR and the learning level achieved by students. There was no evidence of negative effects under the particular imposed conditions, because of sanitary restrictions when implementing the mAR.

On the application effect of mobile augmented reality technology in the teaching of neuroanatomy / 中华医学教育探索杂志

Objective:To explore the application of mobile augmented reality (mAR) technology in the teaching of neuroanatomy, and to observe its effect on students' academic performance and cognitive load.Methods:By collecting and designing various neuroanatomy multimedia teaching resources (graphics, animations and videos), using augmented reality (AR) marker-based image recognition technology, the multimedia resources were placed at the tags in the traditional book pages to make the books interactive. And various multimedia resources were combined with traditional printed books through mobile devices. Forty students were randomized into the experimental group or the control group. The experimental group was taught with mAR multimedia materials, and the control group adopted traditional teaching methods. After a 6-hour course was completed, all students had a unified test, and the academic performance test and the PAAS(platform-as-a-service) cognitive load scale were used for data collection and analysis. The variance analyses (MANOVA and ANOVA) were used for significance testing.Results:One-way MANOVA test was used to determine the learning effect of mAR on academic performance and cognitive load. The results showed that there was a significant difference between the experimental group and the control group ( P<0.05). The univariate ANOVA test found that the experimental group students who learned neuroanatomy through mAR had better test scores than the control group students. In addition, compared with the control group students, the cognitive load of students in experimental group was significantly reduced, with statistical significance (all P<0.05). Conclusion:Through the teaching practice, we found that using mAR to learn neuroanatomy helps students improve their academic performance while reducing their cognitive load.

Mobile augmented reality based indoor map for improving geo-visualization.

Unlike traditional visualization methods, augmented reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. The integration of AR and GIS is a promising way to display spatial information in context. However, most existing AR-GIS applications only provide local spatial information in a fixed location, which is exposed to a set of problems, limited legibility, information clutter and the incomplete spatial relationships. In addition, the indoor space structure is complex and GPS is unavailable, so that indoor AR systems are further impeded by the limited capacity of these systems to detect and display location and semantic information. To address this problem, the localization technique for tracking the camera positions was fused by Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). The multi-sensor fusion-based algorithm employs a particle filter. Based on the direction and position of the phone, the spatial information is automatically registered onto a live camera view. The proposed algorithm extracts and matches a bounding box of the indoor map to a real world scene. Finally, the indoor map and semantic information were rendered into the real world, based on the real-time computed spatial relationship between the indoor map and live camera view. Experimental results demonstrate that the average positioning error of our approach is 1.47 m, and 80% of proposed method error is within approximately 1.8 m. The positioning result can effectively support that AR and indoor map fusion technique links rich indoor spatial information to real world scenes. The method is not only suitable for traditional tasks related to indoor navigation, but it is also promising method for crowdsourcing data collection and indoor map reconstruction.

Effects of using mobile augmented reality for simple interest computation in a financial mathematics course.

Understanding the concept of simple interest is essential in financial mathematics because it establishes the basis to comprehend complex conceptualizations. Nevertheless, students often have problems learning about simple interest. This paper aims to introduce a prototype called "simple interest computation with mobile augmented reality" (SICMAR) and evaluate its effects on students in a financial mathematics course. The research design comprises four stages: (i) planning; (ii) hypotheses development; (iii) software development; and (iv) design of data collection instruments. The planning stage explains the problems that students confront to learn about simple interest. In the second stage, we present the twelve hypotheses tested in the study. The stage of software development discusses the logic implemented for SICMAR functionality. In the last stage, we design two surveys and two practice tests to assess students. The pre-test survey uses the attention, relevance, confidence, and satisfaction (ARCS) model to assess students' motivation in a traditional learning setting. The post-test survey assesses motivation, technology usage with the technology acceptance model (TAM), and prototype quality when students use SICMAR. Also, students solve practice exercises to assess their achievement. One hundred three undergraduates participated in both sessions of the study. The findings revealed the direct positive impact of SICMAR on students' achievement and motivation. Moreover, students expressed their interest in using the prototype because of its quality. In summary, students consider SICMAR as a valuable complementary tool to learn simple interest topics.

Effects of mobile augmented reality apps on impulse buying behavior: An investigation in the tourism field.

Many of today's online services are designed specifically to encourage impulse buying. Moreover, many studies have shown that with the assistance of Mobile Augmented Reality, retailers have the potential to significantly improve their sales. However, the effects of Mobile AR on consumer impulse buying behavior have yet to be examined, particularly in the tourism field. Consequently, the present study integrates the Technology Acceptance Model (TAM), Stimulus-Organism-Response (SOR) framework, and flow theory to examine the effects of Mobile AR apps on tourist impulse buyingbehavior. The research model is implemented using an online questionnaire, with the results analyzed by Partial-Least-Squares Structural Equation Modeling (PLS-SEM) approach. The results obtained from 479 valid samples show that the characteristics of Mobile AR apps play an important role in governing tourist behavior in making unplanned purchases. In particular, as the utility, ease-of-use, and interactivity of the apps increase, the perceived enjoyment and satisfaction of the user also increase and give rise to a stronger impulse buying behavior. The results also reveal a mediating effect of the flow experience on the relationship between the perceived ease of use of the Mobile AR app and the user satisfaction in using the app. Overall, the findings presented in this study provide a useful source of reference for Mobile AR app developers, retailers, and tourism marketers in better understanding users' preferences for Mobile AR apps and strengthening their impulse buying behavior in the tourism context as a result.

Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory.

In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of 3D models based on design artefacts based on typical design layout plan design symbols and allows the user to investigate different design alternatives. We applied John Keller's Attention, Relevance, Confidence, and Satisfaction (ARCS) learning motivation model to validate our solution to examine the students' willingness and verify the ability of students to improve learning through MAR technology. We compared a sample experimental group of N = 52 test-subjects with a sample of N = 48 candidates in a control group. Learning indicators as learning interest, confidence, satisfaction and effective have been utilized to assess the students' learning motivation through the use of MAR technology. The learning results have been determined by the independent sample t testing. The significance of the post-test had a p-value < 0.05 difference. The result of the study clearly shows that the reference group utilizing MAR technology as a learning aid show a higher learning effectiveness as the control group. Thus, we conclude that MAR technology does enhance students' learning ability for interior design and making appropriate design decisions.

Exploring Smart Glasses for Augmented Reality: A Valuable and Integrative Tool in Precision Livestock Farming.

The growing interest in Augmented Reality (AR) systems is becoming increasingly evident in all production sectors. However, to the authors' knowledge, a literature gap has been found with regard to the application of smart glasses for AR in the agriculture and livestock sector. In fact, this technology allows farmers to manage animal husbandry in line with precision agriculture principles. The aim of this study was to evaluate the performances of an AR head-wearable device as a valuable and integrative tool in precision livestock farming. In this study, the GlassUp F4 Smart Glasses (F4SG) for AR were explored. Laboratory and farm tests were performed to evaluate the implementation of this new technology in livestock farms. The results highlighted several advantages of F4SG applications in farm activities. The clear and fast readability of the information related to a single issue, combined with the large number of readings that SG performed, allowed F4SG adoption even in large farms. In addition, the 7 h of battery life and the good quality of audio-video features highlighted their valuable attitude in remote assistance, supporting farmers on the field. Nevertheless, other studies are required to provide more findings for future development of software applications specifically designed for agricultural purposes.

A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications.

The adaptability between different environments remains a challenge for Mobile Augmented Reality (MAR). If not done seamlessly, such transitions may cause discontinuities in navigation, consequently disorienting users and undermining the acceptance of this technology. The transition between environments is hard because there are currently no localization techniques that work well in any place: sensor-based applications can be harmed by obstacles that hamper sensor communication (e.g., GPS) and by infrastructure limitations (e.g., Wi-Fi), and image-based applications can be affected by lighting conditions that impair computer vision techniques. Hence, this paper presents an adaptive model to perform transitions between different types of environments for MAR applications. The model has a hybrid approach, choosing the best combination of long-range sensors, short-range sensors, and computer vision techniques to perform fluid transitions between environments that mitigate problems in location, orientation, and registration. To assess the model, we developed a MAR application and conducted a navigation test with volunteers to validate transitions between outdoor and indoor environments, followed by a short interview. The results show that the transitions were well succeeded, since the application self-adapted to the studied environments, seamlessly changing sensors when needed.

Mobile Augmented Reality as Usability to Enhance Nurse Prevent Violence Learning Satisfaction.

Violence in hospitals, nurses are at high risk of patient's aggression in the workplace. This learning course application Mobile Augmented Reality to enhance nurse to prevent violence skill. Increasingly, mobile technologies introduced and integrated into classroom teaching and clinical applications. Improving the quality of learning course and providing new experiences for nurses.

Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system.

In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors' method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.

Quantifying attention shifts in augmented reality image-guided neurosurgery.

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures.

Mobile Augmented Reality enhances indoor navigation for wheelchair users

Introduction: Individuals with mobility impairments associated with lower limb disabilities often face enormous challenges to participate in routine activities and to move around various environments. For many, the use of wheelchairs is paramount to provide mobility and social inclusion. Nevertheless, they still face a number of challenges to properly function in our society. Among the many difficulties, one in particular stands out: navigating in complex internal environments (indoors). The main objective of this work is to propose an architecture based on Mobile Augmented Reality to support the development of indoor navigation systems dedicated to wheelchair users, that is also capable of recording CAD drawings of the buildings and dealing with accessibility issues for that population. Methods Overall, five main functional requirements are proposed: the ability to allow for indoor navigation by means of Mobile Augmented Reality techniques; the capacity to register and configure building CAD drawings and the position of fiducial markers, points of interest and obstacles to be avoided by the wheelchair user; the capacity to find the best route for wheelchair indoor navigation, taking stairs and other obstacles into account; allow for the visualization of virtual directional arrows in the smartphone displays; and incorporate touch or voice commands to interact with the application. The architecture is proposed as a combination of four layers: User interface; Control; Service; and Infrastructure. A proof-of-concept application was developed and tests were performed with disable volunteers operating manual and electric wheelchairs. Results The application was implemented in Java for the Android operational system. A local database was used to store the test building CAD drawings and the position of fiducial markers and points of interest. The Android Augmented Reality library was used to implement Augmented Reality and the Blender open source library handled the basis for implementing directional navigation arrows. OpenGL ES provided support for various graphics and mathematical transformations for embedded systems, such as smartphones. Experiments were performed in an academic building with various labs, classrooms and male and female bathrooms. Two disable volunteers using wheelchairs showed no difficulties to interact with the application, either by entering touch or voice commands, and to navigate within the testing environment with the help of the navigational arrows implemented by the augmented reality modules. Conclusion The novel features implemented in the proposed architecture, with special emphasis on the use of Mobile Augmented Reality and the ability to identify the best routes free of potential hazards for wheelchair users, were capable of providing significant benefits for wheelchair indoor navigation when compared to current techniques described in the literature.

Learning anatomy via mobile augmented reality: Effects on achievement and cognitive load.

Augmented reality (AR), a new generation of technology, has attracted the attention of educators in recent years. In this study, a MagicBook was developed for a neuroanatomy topic by using mobile augmented reality (mAR) technology. This technology integrates virtual learning objects into the real world and allow users to interact with the environment using mobile devices. The purpose of this study was to determine the effects of learning anatomy via mAR on medical students' academic achievement and cognitive load. The mixed method was applied in the study. The random sample consisted of 70 second-year undergraduate medical students: 34 in an experimental group and 36 in a control group. Academic achievement test and cognitive load scale were used as data collection tool. A one-way MANOVA test was used for analysis. The experimental group, which used mAR applications, reported higher achievement and lower cognitive load. The use of mAR applications in anatomy education contributed to the formation of an effective and productive learning environment. Student cognitive load decreased as abstract information became concrete in printed books via multimedia materials in mAR applications. Additionally, students were able to access the materials in the MagicBook anytime and anywhere they wanted. The mobile learning approach helped students learn better by exerting less cognitive effort. Moreover, the sensory experience and real time interaction with environment may provide learning satisfaction and enable students to structure their knowledge to complete the learning tasks. Anat Sci Educ 9: 411-421. © 2016 American Association of Anatomists.

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters.